Question

You run a correlation matrix between a Y variables auto sales in units and two X variables auto prices (X1) and car buyer’s income (X2). As expected auto prices had a high negative correlation to auto sales while buyer’s income had a high positive correlation. Both X variables had significant correlations. When you run a multiple regression analysis of the forecast variable auto sales with independent variables automobile price and car buyer’s income the results were positive coefficients for both price and income. Not only that, the automobile price coefficient was found not to be significant. Is this what you would expect and what is the likely cause?

Question 20 options:

	1) Yes, you would expect this as a result of serial correlation.
	2) Yes, you would expect this since you cannot tell regression model outcome from correlations.
	3) No, the switch in expected sign and lowered significance is likely caused by serial correlation.
	4) No. the lowered significance and sign switch is likely caused by multicollinearity.

Answer 1

Correct option: (4) No, the lowered significance and sign switch is likely caused by multicollinearity

Reason: The opposite sign as well as reduced significance of the variable is likely because of the problem of multicollinearity which could have been present in the model.

Multicollinearity can be present as consumer income and auto prices are correlated. As consumer incomes increase, prices of automobiles will also see a sharp increase.